loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Christopher C. Yang 1 and Y. C. Wong 2

Affiliations: 1 College of Information Science and Technology, Drexel University; Chinese University of Hong Kong, Hong Kong ; 2 Chinese University of Hong Kong, Hong Kong

ISBN: 978-989-8111-27-2

Keyword(s): Class association rules mining, market intelligence, Web content mining, knowledge management.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Soft Computing ; Symbolic Systems ; Web Mining

Abstract: The Web has provided an excellent platform for business to consumer (B2C) electronic commerce. B2C electronic commerce offers convenience, choice, lower cost and customization to consumers.The electronic shopping platform allows consumers to make intelligent comparison and purchasing decision on consumer products. In addition to comparing product specifications as described on electronic catalogue for better purchasing decision, consumers also hunger for consumer reviews to identify the best products that fit their preferences. For example, a professional photographer would like to identify a camera with lens of high quality and zooming power but a general user may like to find a camera that is cheap, light, and with a large LCD screen. When consumers take consumer reviews as reference, they are interested in both opinion orientation and product features that they are describing. Most of the prior works on consumer opinions mining focus on identifying opinion orientation. Some recent works have started to classify product features but heavily rely on linguistic and natural language processing techniques. However, the writing in consumer reviews is usually less formal and many of them do not conform to the grammatical rules. Therefore, the linguistic and language processing approach is not satisfactory. In this work, we propose a sentiment analysis system to classify product features of consumer reviews by mining class association rules. The experimental result shows that the performance is promising. The content mining approach outperforms the natural language processing approach. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.172.195.49

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
C. Yang C.; C. Wong Y. and (2008). MINING CONSUMER OPINIONS FROM THE WEB.In Proceedings of the Fourth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-8111-27-2, pages 187-192. DOI: 10.5220/0001523201870192

@conference{webist08,
author={Christopher {C. Yang} and Y. {C. Wong}},
title={MINING CONSUMER OPINIONS FROM THE WEB},
booktitle={Proceedings of the Fourth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2008},
pages={187-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001523201870192},
isbn={978-989-8111-27-2},
}

TY - CONF

JO - Proceedings of the Fourth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - MINING CONSUMER OPINIONS FROM THE WEB
SN - 978-989-8111-27-2
AU - C. Yang, C.
AU - C. Wong, Y.
PY - 2008
SP - 187
EP - 192
DO - 10.5220/0001523201870192

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.